Disability–free survival after coronary artery bypass grafting in women and men with heart failure
Bibliographic record
Abstract
Objective: Heart failure (HF) impairs survival post coronary artery bypass grafting (CABG), but little is known about the postoperative quality of life (QoL) in patients with HF. We derived a patient-centred QoL surrogate and assessed the impact of different HF subtypes on this surrogate in the year post-CABG. Methods: We surveyed 3112 cardiovascular patients to derive a patient-centred disability outcome and studied this outcome in a population-based cohort. We defined preserved ejection fraction as ≥50% and reduced ejection fraction as <50%. The primary outcome was disability, defined according to compiled patient-derived values. The secondary outcomes consisted of each individual component of disability, and death. The incidence of disability was calculated using cumulative incidence functions, with death as a competing risk. We identified predictors of disability using cause-specific hazard models. Results: Patient-derived disability outcome consisted of stroke, nursing home admission and recurrent hospitalisations. When applied to 40 083 CABG patients (20.6% women), the incidence of disability was 5.4% while the incidence of death was 3.7% in the year post-CABG. Female sex was associated with an adjusted HR of 1.25 (95% CI 1.13 to 1.37) for disability. Women with HF with preserved ejection fraction had an adjusted HR of 1.73 (95% CI 1.52 to 1.98) for disability. Conclusions: Disability was a more frequent complication than death in the year post-CABG. Women experienced higher burden of disability than men, and female sex and the presence of HF were important disability risk factors. Efforts should be dedicated to disability risk prediction to enable patient-centred operative decision-making and to developing sex-specific treatment strategies to improve outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".